Water status and yield response to deficit irrigation and fertilization of three olive oil cultivars under the semi-arid conditions of Tunisia

Sustainability of olive production is possible by adopting the modern techniques of irrigation and fertilization. In Tunisia, olive trees are usually cultivated in poor soils, under semi-arid conditions characterized by water scarcity. This study investigated the effects of different water supply and fertilization on leaf water status and crop yield of three different olive oil varieties cultivated in central Tunisia, during four experimental seasons (2014-2017). Three treatments were examined: trees conducted under rainfed conditions (TRF), which represented the control treatment, trees irrigated with 50% ETc (T50) and, finally, trees irrigated with 50% ETc and with additional fertilization (T50F). Leaf water content and potential, yield and water use efficiency have been monitored on three different varieties, Chetoui, Chemlali, and Koroneiki, which are quite typical in the considered region. For all the growing seasons, midday leaf water potentials were measured from April to September. Midday leaf water potentials (MLWP) were generally higher for the two irrigated treatments (T50 and T50F) than for non-irrigated trees (TRF). As the season proceeded, MLWPs tended to decrease during summer for all the treatments and varieties. The lowest values were observed for the non-irrigated trees, varying between -3.25 MPa to -4.75 MPa. Relative leaf water content followed the same trends of midday leaf water potentials. Chetoui showed the lowest yield, which did not exceed 1530 Kg/(ha year), even for irrigated and fertilized trees. On the other hand, the yields of Chemlali and Koroneiki, cumulated in the four years, reached the maximum value of about 20 tons/ha. For these two varieties, the cumulated yield obtained in the control treatment (TRF) resulted significantly lower than the corresponding of the other two treatments (T50 and T50F). The highest irrigation water use efficiency (WUE) was estimated for Chemlali (T50) and (TRF). WUE was equal to 1.22 Kg/m3 for Koroneiki under fertigated treatment (T50F). Application of the only water supply (50% ETc) or associated with fertilizer improved the tree water status and increased the productivity of Chemlali and Koroneiki varieties

Regional projections of temperature and precipitation changes: Robustness and uncertainty aspects

This study presents the analysis of bias-corrected projections of changes in temperature and precipitation in the Vistula and Odra basins, covering approximately 90% of the Polish territory and small parts of neighbouring countries in Central and Eastern Europe. The ensemble of climate projections consists of nine regional climate model simulations from the EURO-CORDEX ensemble for two future periods 2021-2050 and 2071-2100, assuming two representative concentration pathways (RCPs) 4.5 and 8.5. The robustness is measured by the ensemble models' agreement on significant changes.We found a robust increase in the annual mean of daily minimum and maximum temperature, by 1-1.4 °C in the near future and by 1.9-3.8 °C in the far future (areal-means of the ensemble mean values). Higher increases are consistently associated with minimum temperature and the gradient of change goes from SWto NE regions. Seasonal projections of both temperature variables reflect lower robustness and suggest a higher future increase in winter temperatures than in other seasons, notably in the far future under RCP 8.5 (by more than 1 °C). However, changes in annual means of precipitation are uncertain and not robust in any of the analysed cases, even though the climate models agree well on the increase. This increase is intensified with rising global temperatures and varies from 5.5% in the near future under RCP 4.5 to 15.2%in the far future under RCP 8.5. Spatial variability is substantial, although quite variable between individual climate model simulations. Although seasonal means of precipitation are projected to considerably increase in all four combinations of RCPs and projection horizons for winter and spring, the high model spread reduces considerably the robustness, especially for the far future. In contrast, the ensemble members agree well that overall, the summer and autumn (with exception of the far future under RCP 8.5) precipitation will not undergo statistically significant changes

Features selection approaches for an objective control of cosmetic quality of coated surfaces

The cosmetic aspect is one of the main functions of industrial surfaces in numerous applications. Even the smallest surface defects may have a critical effect on the cosmetic tolerability of such industrial surfaces. Thus, surfaces are generally coated at the last manufacturing process stage to cover existing defects and to certify their cosmetic quality. The surface quality is however constantly controlled after coating that induces an increase of lead-time increase and production costs. This is due to a various flaw patterns and a lack of uncoated surfaces specifications. Hence, the identification of relevant surface morphological parameters underlies an objective and automatic cosmetic control performance. In fact, this relevant parameter selection allows tracking surface flaws during the coating finishing operation. This paper presents a comprehensive overview of various feature selection tools for data analysis (Neighbourhood Component Analysis (NCA), ReliefF, Sequential wrapper method, Decision tree) to extract relevant information out of physical data. A design of experiment based on scratches test on amorphous polymers to generate typical controlled defects has been performed. Then, several cosmetic defects characteristics were extracted from experimental measurements. Feature selection approaches were applied and compared to determine the most relevant parameters. The advantages and limitations of each method for data analysis have been highlighted in the case of real engineering surface quality control

WTC2005-64215 MULTI SCALE STUDY OF ABRASION SIGNATURE BY 2D WAVELET DECOMPOSITION

Abstract The high performance of industrial applications, requires increasingly technical functional surfaces, particulary from the point of view of topography and microtexture. To study the effect of abrasive finishing in a wide range of wavelengths of surface topography, we developed a multi-scale approach, based on the decomposition of surface topography by 2D continuous wavelet transform. This new approach made it possible to determine the multi-scale transfer function of machining by abrasion for each stage of finishing. The methodology can be extended to characterize abrasive wear in a wide range of scales. Introduction The use of hard turning as a finishing process is often limited by surface quality requirements in the case of component surfaces designed to support high stress. Low roughness can be achieved only at low feed rates. Moreover, tool wear leads to a deterioration in the surface after the tool has been in use for some time. A subsequent finishing operation can both increase the range of permissible feed rates towards higher values and prolong the life of the tool's cutting edge. One finishing operation whose working principle suits especially well for combination with hard turning is the belt grinding. This abrasive operation makes it possible to create surfaces of high quality, with specific functions like mechanical bearing pressure, sealing of metal joints, friction and noise of friction. Abrasive finishing modifies the surface topography in a wide range of scales of roughness and waviness, and consequently modifies the functionality of the surface in terms of bearing area, local plasticity and durability. This paper introduces a new approach based on a multi-scale decomposition of the surface topography before and after finishing by using a 2D continuous wavelet decomposition. This approach makes it possible to follow the effect of the various stages of finishing on a wide range of wavelengths, and makes it possible to determine th

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yields an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down

Subsampling impact on the climate change signal over poland based on simulations from statistical and dynamical downscaling

Most impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical-statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071-2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071-2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal. © 2019 American Meteorological Society

CHASE-PL Climate Projection dataset over Poland - Bias adjustment of EURO-CORDEX simulations

The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Climate Projections – Gridded Daily Precipitation and Temperature dataset 5 km (CPLCP-GDPT5) consists of projected daily minimum and maximum air temperatures and precipitation totals of nine EURO-CORDEX regional climate model outputs bias corrected and downscaled to a 5 km  ×  5 km grid. Simulations of one historical period (1971–2000) and two future horizons (2021–2050 and 2071–2100) assuming two representative concentration pathways (RCP4.5 and RCP8.5) were produced. We used the quantile mapping method and corrected any systematic seasonal bias in these simulations before assessing the changes in annual and seasonal means of precipitation and temperature over Poland. Projected changes estimated from the multi-model ensemble mean showed that annual means of temperature are expected to increase steadily by 1 °C until 2021–2050 and by 2 °C until 2071–2100 assuming the RCP4.5 emission scenario. Assuming the RCP8.5 emission scenario, this can reach up to almost 4 °C by 2071–2100. Similarly to temperature, projected changes in regional annual means of precipitation are expected to increase by 6 to 10 % and by 8 to 16 % for the two future horizons and RCPs, respectively. Similarly, individual model simulations also exhibited warmer and wetter conditions on an annual scale, showing an intensification of the magnitude of the change at the end of the 21st century. The same applied for projected changes in seasonal means of temperature showing a higher winter warming rate by up to 0.5 °C compared to the other seasons. However, projected changes in seasonal means of precipitation by the individual models largely differ and are sometimes inconsistent, exhibiting spatial variations which depend on the selected season, location, future horizon, and RCP. The overall range of the 90 % confidence interval predicted by the ensemble of multi-model simulations was found to likely vary between −7 % (projected for summer assuming the RCP4.5 emission scenario) and +40 % (projected for winter assuming the RCP8.5 emission scenario) by the end of the 21st century. Finally, this high-resolution bias-corrected product can serve as a basis for climate change impact and adaptation studies for many sectors over Poland. The CPLCP-GDPT5 dataset is publicly available at https://doi.org/10.4121/uuid:e940ec1a-71a0-449e-bbe3-29217f2ba31d